Overview

Dataset statistics

Number of variables13
Number of observations6999
Missing cells203
Missing cells (%)0.2%
Duplicate rows493
Duplicate rows (%)7.0%
Total size in memory711.0 KiB
Average record size in memory104.0 B

Variable types

Text2
Numeric7
Categorical4

Alerts

Dataset has 493 (7.0%) duplicate rowsDuplicates
engine is highly overall correlated with max_power and 1 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 2 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with max_power and 2 other fieldsHigh correlation
transmission is highly overall correlated with max_power and 1 other fieldsHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (51.8%)Imbalance
torque has 203 (2.9%) missing valuesMissing

Reproduction

Analysis started2025-12-01 23:04:05.045100
Analysis finished2025-12-01 23:04:08.113960
Duration3.07 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:08.248767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length42
Mean length25.227747
Min length11

Characters and Unicode

Total characters176569
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique804 ?
Unique (%)11.5%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti2126
 
6.4%
hyundai1197
 
3.6%
swift689
 
2.1%
mahindra666
 
2.0%
bsiv622
 
1.9%
tata611
 
1.8%
diesel583
 
1.8%
1.2505
 
1.5%
vxi485
 
1.5%
plus459
 
1.4%
Other values (828)25224
76.1%
2025-12-02T04:04:08.477112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter89631
50.8%
Uppercase Letter45300
25.7%
Space Separator26169
 
14.8%
Decimal Number12071
 
6.8%
Other Punctuation2232
 
1.3%
Dash Punctuation684
 
0.4%
Close Punctuation241
 
0.1%
Open Punctuation241
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a12741
14.2%
i11534
12.9%
t8812
9.8%
r7759
8.7%
o7165
8.0%
n6573
7.3%
e6564
7.3%
u5091
 
5.7%
d3843
 
4.3%
l3607
 
4.0%
Other values (16)15942
17.8%
Uppercase Letter
ValueCountFrequency (%)
S4755
 
10.5%
I4160
 
9.2%
M3872
 
8.5%
V3606
 
8.0%
D3566
 
7.9%
T3369
 
7.4%
X3092
 
6.8%
C2179
 
4.8%
A1911
 
4.2%
B1876
 
4.1%
Other values (16)12914
28.5%
Decimal Number
ValueCountFrequency (%)
13027
25.1%
02959
24.5%
22454
20.3%
5867
 
7.2%
4830
 
6.9%
8557
 
4.6%
6402
 
3.3%
3401
 
3.3%
7299
 
2.5%
9275
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.2219
99.4%
/13
 
0.6%
Space Separator
ValueCountFrequency (%)
26169
100.0%
Dash Punctuation
ValueCountFrequency (%)
-684
100.0%
Close Punctuation
ValueCountFrequency (%)
)241
100.0%
Open Punctuation
ValueCountFrequency (%)
(241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin134931
76.4%
Common41638
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a12741
 
9.4%
i11534
 
8.5%
t8812
 
6.5%
r7759
 
5.8%
o7165
 
5.3%
n6573
 
4.9%
e6564
 
4.9%
u5091
 
3.8%
S4755
 
3.5%
I4160
 
3.1%
Other values (42)59777
44.3%
Common
ValueCountFrequency (%)
26169
62.8%
13027
 
7.3%
02959
 
7.1%
22454
 
5.9%
.2219
 
5.3%
5867
 
2.1%
4830
 
2.0%
-684
 
1.6%
8557
 
1.3%
6402
 
1.0%
Other values (6)1470
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII176569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.8184
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:08.522020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2015
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0530948
Coefficient of variation (CV)0.0020126417
Kurtosis1.7672148
Mean2013.8184
Median Absolute Deviation (MAD)3
Skewness-1.0773023
Sum14094715
Variance16.427578
MonotonicityNot monotonic
2025-12-02T04:04:08.567221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017870
12.4%
2016736
10.5%
2018704
10.1%
2015662
9.5%
2013581
8.3%
2012563
8.0%
2014532
7.6%
2019511
7.3%
2011499
7.1%
2010336
 
4.8%
Other values (19)1005
14.4%
ValueCountFrequency (%)
19831
 
< 0.1%
19911
 
< 0.1%
19943
 
< 0.1%
19951
 
< 0.1%
19963
 
< 0.1%
199710
0.1%
19989
0.1%
199913
0.2%
200021
0.3%
20018
 
0.1%
ValueCountFrequency (%)
202069
 
1.0%
2019511
7.3%
2018704
10.1%
2017870
12.4%
2016736
10.5%
2015662
9.5%
2014532
7.6%
2013581
8.3%
2012563
8.0%
2011499
7.1%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639515.2
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:08.617894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile110000
Q1254999
median450000
Q3675000
95-th percentile1925000
Maximum10000000
Range9970001
Interquartile range (IQR)420001

Descriptive statistics

Standard deviation808941.91
Coefficient of variation (CV)1.2649299
Kurtosis21.308644
Mean639515.2
Median Absolute Deviation (MAD)200000
Skewness4.2107557
Sum4.4759669 × 109
Variance6.5438702 × 1011
MonotonicityNot monotonic
2025-12-02T04:04:08.679407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000196
 
2.8%
600000181
 
2.6%
450000178
 
2.5%
350000178
 
2.5%
550000175
 
2.5%
650000161
 
2.3%
250000153
 
2.2%
400000153
 
2.2%
500000152
 
2.2%
200000138
 
2.0%
Other values (627)5334
76.2%
ValueCountFrequency (%)
299991
 
< 0.1%
300002
 
< 0.1%
315041
 
< 0.1%
350003
 
< 0.1%
390001
 
< 0.1%
4000012
0.2%
420002
 
< 0.1%
4500016
0.2%
459572
 
< 0.1%
5000015
0.2%
ValueCountFrequency (%)
100000001
 
< 0.1%
72000001
 
< 0.1%
65230001
 
< 0.1%
62230001
 
< 0.1%
60000004
 
0.1%
59230001
 
< 0.1%
58500001
 
< 0.1%
58300002
 
< 0.1%
58000002
 
< 0.1%
550000027
0.4%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69584.616
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:08.734853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9000
Q135000
median60000
Q397000
95-th percentile150000
Maximum2360457
Range2360456
Interquartile range (IQR)62000

Descriptive statistics

Standard deviation57724.002
Coefficient of variation (CV)0.82955121
Kurtosis410.79799
Mean69584.616
Median Absolute Deviation (MAD)30000
Skewness12.069152
Sum4.8702272 × 108
Variance3.3320604 × 109
MonotonicityNot monotonic
2025-12-02T04:04:08.791833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000464
 
6.6%
70000389
 
5.6%
80000387
 
5.5%
60000367
 
5.2%
50000343
 
4.9%
100000306
 
4.4%
90000291
 
4.2%
110000264
 
3.8%
40000260
 
3.7%
30000215
 
3.1%
Other values (817)3713
53.1%
ValueCountFrequency (%)
11
 
< 0.1%
10006
 
0.1%
13001
 
< 0.1%
13034
 
0.1%
15003
 
< 0.1%
16001
 
< 0.1%
16201
 
< 0.1%
200028
0.4%
21181
 
< 0.1%
21361
 
< 0.1%
ValueCountFrequency (%)
23604571
< 0.1%
15000001
< 0.1%
5774141
< 0.1%
5000002
< 0.1%
4750001
< 0.1%
4400001
< 0.1%
4260001
< 0.1%
3800001
< 0.1%
3764121
< 0.1%
3700001
< 0.1%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Diesel
3793 
Petrol
3120 
CNG
 
52
LPG
 
34

Length

Max length6
Median length6
Mean length5.9631376
Min length3

Characters and Unicode

Total characters41736
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel3793
54.2%
Petrol3120
44.6%
CNG52
 
0.7%
LPG34
 
0.5%

Length

2025-12-02T04:04:08.844226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T04:04:08.883233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel3793
54.2%
petrol3120
44.6%
cng52
 
0.7%
lpg34
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34565
82.8%
Uppercase Letter7171
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10706
31.0%
l6913
20.0%
i3793
 
11.0%
s3793
 
11.0%
t3120
 
9.0%
r3120
 
9.0%
o3120
 
9.0%
Uppercase Letter
ValueCountFrequency (%)
D3793
52.9%
P3154
44.0%
G86
 
1.2%
C52
 
0.7%
N52
 
0.7%
L34
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin41736
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII41736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Individual
5826 
Dealer
967 
Trustmark Dealer
 
206

Length

Max length16
Median length10
Mean length9.6239463
Min length6

Characters and Unicode

Total characters67358
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual5826
83.2%
Dealer967
 
13.8%
Trustmark Dealer206
 
2.9%

Length

2025-12-02T04:04:08.921491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T04:04:08.950947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual5826
80.9%
dealer1173
 
16.3%
trustmark206
 
2.9%

Most occurring characters

ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
n5826
8.6%
v5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59947
89.0%
Uppercase Letter7205
 
10.7%
Space Separator206
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d11652
19.4%
i11652
19.4%
a7205
12.0%
l6999
11.7%
u6032
10.1%
n5826
9.7%
v5826
9.7%
e2346
 
3.9%
r1585
 
2.6%
s206
 
0.3%
Other values (3)618
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
I5826
80.9%
D1173
 
16.3%
T206
 
2.9%
Space Separator
ValueCountFrequency (%)
206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin67152
99.7%
Common206
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d11652
17.4%
i11652
17.4%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.7%
n5826
8.7%
v5826
8.7%
e2346
 
3.5%
r1585
 
2.4%
Other values (6)2203
 
3.3%
Common
ValueCountFrequency (%)
206
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII67358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
n5826
8.6%
v5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Manual
6095 
Automatic
904 

Length

Max length9
Median length6
Mean length6.3874839
Min length6

Characters and Unicode

Total characters44706
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual6095
87.1%
Automatic904
 
12.9%

Length

2025-12-02T04:04:08.991966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T04:04:09.226091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual6095
87.1%
automatic904
 
12.9%

Most occurring characters

ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37707
84.3%
Uppercase Letter6999
 
15.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a13094
34.7%
u6999
18.6%
n6095
16.2%
l6095
16.2%
t1808
 
4.8%
o904
 
2.4%
m904
 
2.4%
i904
 
2.4%
c904
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
M6095
87.1%
A904
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Latin44706
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
First Owner
4587 
Second Owner
1791 
Third Owner
473 
Fourth & Above Owner
 
144
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.442778
Min length11

Characters and Unicode

Total characters80088
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner4587
65.5%
Second Owner1791
 
25.6%
Third Owner473
 
6.8%
Fourth & Above Owner144
 
2.1%
Test Drive Car4
 
0.1%

Length

2025-12-02T04:04:09.265878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T04:04:09.299597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
owner6995
49.0%
first4587
32.1%
second1791
 
12.5%
third473
 
3.3%
fourth144
 
1.0%
144
 
1.0%
above144
 
1.0%
test4
 
< 0.1%
drive4
 
< 0.1%
car4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58507
73.1%
Uppercase Letter14146
 
17.7%
Space Separator7291
 
9.1%
Other Punctuation144
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r12207
20.9%
e8938
15.3%
n8786
15.0%
w6995
12.0%
i5064
8.7%
t4735
 
8.1%
s4591
 
7.8%
d2264
 
3.9%
o2079
 
3.6%
c1791
 
3.1%
Other values (5)1057
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
O6995
49.4%
F4731
33.4%
S1791
 
12.7%
T477
 
3.4%
A144
 
1.0%
D4
 
< 0.1%
C4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7291
100.0%
Other Punctuation
ValueCountFrequency (%)
&144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72653
90.7%
Common7435
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r12207
16.8%
e8938
12.3%
n8786
12.1%
O6995
9.6%
w6995
9.6%
i5064
7.0%
t4735
 
6.5%
F4731
 
6.5%
s4591
 
6.3%
d2264
 
3.1%
Other values (12)7347
10.1%
Common
ValueCountFrequency (%)
7291
98.1%
&144
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII80088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

mileage
Real number (ℝ)

Distinct375
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.42295
Minimum0
Maximum42
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:09.344432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.99
Q116.8
median19.3
Q322.15
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.35

Descriptive statistics

Standard deviation3.9869306
Coefficient of variation (CV)0.20526905
Kurtosis0.78610405
Mean19.42295
Median Absolute Deviation (MAD)2.7
Skewness-0.15084575
Sum135941.23
Variance15.895616
MonotonicityNot monotonic
2025-12-02T04:04:09.392097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3259
 
3.7%
18.9197
 
2.8%
19.7150
 
2.1%
18.6139
 
2.0%
21.1131
 
1.9%
17116
 
1.7%
15.96105
 
1.5%
17.8101
 
1.4%
2299
 
1.4%
12.9995
 
1.4%
Other values (365)5607
80.1%
ValueCountFrequency (%)
016
0.2%
94
 
0.1%
9.55
 
0.1%
102
 
< 0.1%
10.12
 
< 0.1%
10.514
0.2%
10.711
 
< 0.1%
10.752
 
< 0.1%
10.81
 
< 0.1%
10.94
 
0.1%
ValueCountFrequency (%)
421
 
< 0.1%
33.443
 
< 0.1%
331
 
< 0.1%
32.521
 
< 0.1%
30.462
 
< 0.1%
28.480
1.1%
28.0936
0.5%
27.625
 
0.1%
27.46
 
0.1%
27.3949
0.7%

engine
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1452.2569
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:09.441699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2498
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation495.1513
Coefficient of variation (CV)0.34095297
Kurtosis0.86257694
Mean1452.2569
Median Absolute Deviation (MAD)245
Skewness1.1818336
Sum10164346
Variance245174.81
MonotonicityNot monotonic
2025-12-02T04:04:09.496964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12481087
 
15.5%
1197715
 
10.2%
998393
 
5.6%
796375
 
5.4%
2179330
 
4.7%
1498318
 
4.5%
1396264
 
3.8%
1199233
 
3.3%
2494188
 
2.7%
2523171
 
2.4%
Other values (110)2925
41.8%
ValueCountFrequency (%)
62416
 
0.2%
7935
 
0.1%
796375
5.4%
79966
 
0.9%
81499
 
1.4%
9092
 
< 0.1%
93631
 
0.4%
99324
 
0.3%
99541
 
0.6%
998393
5.6%
ValueCountFrequency (%)
36045
 
0.1%
34981
 
< 0.1%
31983
 
< 0.1%
29993
 
< 0.1%
29972
 
< 0.1%
299314
0.2%
29879
 
0.1%
298228
0.4%
29679
 
0.1%
295615
0.2%

max_power
Real number (ℝ)

High correlation 

Distinct312
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.302923
Minimum32.8
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:09.545300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.8
5-th percentile47.3
Q168.085
median82
Q3100.6
95-th percentile171.5
Maximum400
Range367.2
Interquartile range (IQR)32.515

Descriptive statistics

Standard deviation35.24864
Coefficient of variation (CV)0.38606256
Kurtosis4.1427826
Mean91.302923
Median Absolute Deviation (MAD)14.95
Skewness1.7026217
Sum639029.15
Variance1242.4666
MonotonicityNot monotonic
2025-12-02T04:04:09.592571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74330
 
4.7%
82300
 
4.3%
81.8193
 
2.8%
88.5189
 
2.7%
67149
 
2.1%
46.3139
 
2.0%
62.1130
 
1.9%
67.1127
 
1.8%
67.04126
 
1.8%
88.7125
 
1.8%
Other values (302)5191
74.2%
ValueCountFrequency (%)
32.82
 
< 0.1%
34.219
 
0.3%
3516
 
0.2%
35.52
 
< 0.1%
3774
1.1%
37.488
 
0.1%
37.56
 
0.1%
381
 
< 0.1%
38.42
 
< 0.1%
40.33
 
< 0.1%
ValueCountFrequency (%)
4001
 
< 0.1%
2821
 
< 0.1%
2805
0.1%
2721
 
< 0.1%
270.93
< 0.1%
2651
 
< 0.1%
261.46
0.1%
2582
 
< 0.1%
254.83
< 0.1%
254.791
 
< 0.1%

torque
Text

Missing 

Distinct419
Distinct (%)6.2%
Missing203
Missing (%)2.9%
Memory size54.8 KiB
2025-12-02T04:04:09.724191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length25
Mean length16.250441
Min length5

Characters and Unicode

Total characters110438
Distinct characters34
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)1.5%

Sample

1st row190Nm@ 2000rpm
2nd row250Nm@ 1500-2500rpm
3rd row22.4 kgm at 1750-2750rpm
4th row11.5@ 4,500(kgm@ rpm)
5th row113.75nm@ 4000rpm
ValueCountFrequency (%)
4000rpm740
 
5.2%
2000rpm688
 
4.8%
3500rpm630
 
4.4%
200nm596
 
4.2%
190nm528
 
3.7%
1750rpm483
 
3.4%
rpm452
 
3.1%
90nm353
 
2.5%
3000rpm285
 
2.0%
4200rpm269
 
1.9%
Other values (409)9336
65.0%
2025-12-02T04:04:09.900203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
022916
20.8%
m13512
12.2%
7591
 
6.9%
17473
 
6.8%
@6915
 
6.3%
p6744
 
6.1%
r6744
 
6.1%
N6130
 
5.6%
25896
 
5.3%
55328
 
4.8%
Other values (24)21189
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number56589
51.2%
Lowercase Letter28443
25.8%
Other Punctuation8599
 
7.8%
Space Separator7591
 
6.9%
Uppercase Letter6203
 
5.6%
Dash Punctuation2316
 
2.1%
Open Punctuation342
 
0.3%
Close Punctuation342
 
0.3%
Math Symbol13
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022916
40.5%
17473
 
13.2%
25896
 
10.4%
55328
 
9.4%
44131
 
7.3%
33282
 
5.8%
72540
 
4.5%
92278
 
4.0%
81487
 
2.6%
61258
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
m13512
47.5%
p6744
23.7%
r6744
23.7%
k426
 
1.5%
g426
 
1.5%
n219
 
0.8%
a186
 
0.7%
t186
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N6130
98.8%
M29
 
0.5%
K11
 
0.2%
G11
 
0.2%
R11
 
0.2%
P11
 
0.2%
Other Punctuation
ValueCountFrequency (%)
@6915
80.4%
.1324
 
15.4%
,341
 
4.0%
/19
 
0.2%
Math Symbol
ValueCountFrequency (%)
+11
84.6%
~2
 
15.4%
Space Separator
ValueCountFrequency (%)
7591
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2316
100.0%
Open Punctuation
ValueCountFrequency (%)
(342
100.0%
Close Punctuation
ValueCountFrequency (%)
)342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75792
68.6%
Latin34646
31.4%

Most frequent character per script

Common
ValueCountFrequency (%)
022916
30.2%
7591
 
10.0%
17473
 
9.9%
@6915
 
9.1%
25896
 
7.8%
55328
 
7.0%
44131
 
5.5%
33282
 
4.3%
72540
 
3.4%
-2316
 
3.1%
Other values (10)7404
 
9.8%
Latin
ValueCountFrequency (%)
m13512
39.0%
p6744
19.5%
r6744
19.5%
N6130
17.7%
k426
 
1.2%
g426
 
1.2%
n219
 
0.6%
a186
 
0.5%
t186
 
0.5%
M29
 
0.1%
Other values (4)44
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII110438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
022916
20.8%
m13512
12.2%
7591
 
6.9%
17473
 
6.8%
@6915
 
6.3%
p6744
 
6.1%
r6744
 
6.1%
N6130
 
5.6%
25896
 
5.3%
55328
 
4.8%
Other values (24)21189
19.2%

seats
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4069153
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-12-02T04:04:09.939174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95430803
Coefficient of variation (CV)0.17649769
Kurtosis4.2811184
Mean5.4069153
Median Absolute Deviation (MAD)0
Skewness2.0598916
Sum37843
Variance0.91070382
MonotonicityNot monotonic
2025-12-02T04:04:09.972944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55595
79.9%
7944
 
13.5%
8208
 
3.0%
4104
 
1.5%
972
 
1.0%
654
 
0.8%
1019
 
0.3%
22
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
22
 
< 0.1%
4104
 
1.5%
55595
79.9%
654
 
0.8%
7944
 
13.5%
8208
 
3.0%
972
 
1.0%
1019
 
0.3%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
1019
 
0.3%
972
 
1.0%
8208
 
3.0%
7944
 
13.5%
654
 
0.8%
55595
79.9%
4104
 
1.5%
22
 
< 0.1%

Interactions

2025-12-02T04:04:07.602616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.454137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.840105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.170889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.535218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.866892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.231659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.661297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.499694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.893745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.226117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.576846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.926722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.291628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.709288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.559176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.934472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.282817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.615085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.971939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.344698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.751323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.614239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.976562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.336438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.666945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.014949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.383448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.802034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.669384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.018287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.386445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.714064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.060243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.425112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.854336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.726376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.073700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.442589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.766323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.114987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.482224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.913073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:05.790309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.126287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.494158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:06.818903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.174392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T04:04:07.540779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-02T04:04:10.005859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetransmissionyear
engine1.0000.4370.2370.732-0.4560.0840.5110.2290.5000.488-0.003
fuel0.4371.0000.0350.2060.2970.0310.2100.1080.1100.0500.122
km_driven0.2370.0351.000-0.033-0.1540.0340.2150.018-0.3580.027-0.620
max_power0.7320.206-0.0331.000-0.3440.0860.2750.2480.6500.5970.213
mileage-0.4560.297-0.154-0.3441.0000.089-0.4340.072-0.0280.2140.294
owner0.0840.0310.0340.0860.0891.0000.0290.1690.3640.1690.269
seats0.5110.2100.2150.275-0.4340.0291.0000.0610.2690.0710.005
seller_type0.2290.1080.0180.2480.0720.1690.0611.0000.2820.3730.185
selling_price0.5000.110-0.3580.650-0.0280.3640.2690.2821.0000.5860.714
transmission0.4880.0500.0270.5970.2140.1690.0710.3730.5861.0000.269
year-0.0030.122-0.6200.2130.2940.2690.0050.1850.7140.2691.000

Missing values

2025-12-02T04:04:07.998261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-02T04:04:08.074447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner23.401248.074.00190Nm@ 2000rpm5.0
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner21.141498.0103.52250Nm@ 1500-2500rpm5.0
2Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner23.001396.090.0022.4 kgm at 1750-2750rpm5.0
3Maruti Swift VXI BSIII2007130000120000PetrolIndividualManualFirst Owner16.101298.088.2011.5@ 4,500(kgm@ rpm)5.0
4Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner20.141197.081.86113.75nm@ 4000rpm5.0
5Maruti Wagon R LXI DUO BSIII200796000175000LPGIndividualManualFirst Owner17.301061.057.507.8@ 4,500(kgm@ rpm)5.0
6Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner16.10796.037.0059Nm@ 2500rpm4.0
7Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner23.591364.067.10170Nm@ 1800-2400rpm5.0
8Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner20.001399.068.10160Nm@ 2000rpm5.0
9Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner19.011461.0108.45248Nm@ 2250rpm5.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
6989Maruti Swift Dzire VDI201562500050000DieselIndividualManualFirst Owner26.591248.074.00190Nm@ 2000rpm5.0
6990Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner18.501197.082.85113.7Nm@ 4000rpm5.0
6991Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner20.51998.067.0490Nm@ 3500rpm5.0
6992Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner17.921086.062.1096.1Nm@ 3000rpm5.0
6993Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner18.90998.067.1090Nm@ 3500rpm5.0
6994Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner18.501197.082.85113.7Nm@ 4000rpm5.0
6995Hyundai Verna CRDi SX2007135000119000DieselIndividualManualFourth & Above Owner16.801493.0110.0024@ 1,900-2,750(kgm@ rpm)5.0
6996Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner19.301248.073.90190Nm@ 2000rpm5.0
6997Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.571396.070.00140Nm@ 1800-3000rpm5.0
6998Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.571396.070.00140Nm@ 1800-3000rpm5.0

Duplicate rows

Most frequently occurring

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats# duplicates
64Honda Amaze V CVT Petrol BSIV20197790007032PetrolTrustmark DealerAutomaticFirst Owner19.001199.088.76110Nm@ 4800rpm5.030
192Lexus ES 300h2019515000020000PetrolDealerAutomaticFirst Owner22.372487.0214.56202Nm@ 3600-5200rpm5.030
183Jaguar XF 2.0 Diesel Portfolio2017320000045000DieselDealerAutomaticFirst Owner19.331999.0177.00430Nm@ 1750-2500rpm5.028
467Toyota Innova 2.5 VX (Diesel) 7 Seater201375000079328DieselTrustmark DealerManualSecond Owner12.992494.0100.60200Nm@ 1200-3600rpm7.028
13BMW X4 M Sport X xDrive20d201954000007500DieselDealerAutomaticFirst Owner16.781995.0190.00400Nm@ 1750-2500rpm5.027
255Maruti Baleno Alpha 1.3201874000038817DieselDealerManualFirst Owner27.391248.074.00190Nm@ 2000rpm5.027
306Maruti Swift AMT ZXI201860000069779PetrolDealerAutomaticFirst Owner22.001197.081.80113Nm@ 4200rpm5.027
363Maruti Wagon R LXI201322500058343PetrolTrustmark DealerManualFirst Owner21.79998.067.0590Nm@ 3500rpm5.027
455Toyota Etios VX201762500025538PetrolTrustmark DealerManualFirst Owner16.781496.088.73132Nm@ 3000rpm5.027
114Hyundai Grand i10 1.2 CRDi Sportz201745000056290DieselDealerManualFirst Owner24.001186.073.97190.24nm@ 1750-2250rpm5.026